A Socially Assistive Robot to Help With Getting Dressed
Bibliographic record
Abstract
Getting dressed is a universally performed daily activity, and has a substantial impact on a person’s well-being. Choosing appropriate outfits to wear is important, as clothes protect a person from elements in the environment, and act as a barrier against harsh surfaces [1]. Studies have shown strong correlation between clothing choices and perceptions of sociability, emotional stability, and impression formation (e.g., [2]). This activity, however, can be difficult for some individuals, as they may lack the required reasoning and judgement required [3]. They include children with intellectual and learning disabilities [4] (e.g., Down syndrome [5], dyspraxia [6], autism spectrum disorder [7]), and older adults suffering from dementia including Alzheimer’s disease [8,9], or HIV-associated neurocognitive disorders [10]. In this paper, we present the development of a novel autonomous robotic clothing recommendation system to provide appropriate clothing options, which are personalized to a user’s wardrobe. This research expands on our previous work on socially assistive robots providing assistance with other daily activities, including meal eating [11] and playing Bingo games [12]. Currently, a few smartphone applications exist for providing outfit choices (e.g., [13,14]); however, unlike our proposed system, they are fashion-focused and not able to adapt online to a user’s preferences. Furthermore, by utilizing a socially assistive robot, we provide a more engaging interaction. We utilize the small Nao social robot, Leia, to guide and interact with a user in order to obtain information regarding his/her preferences, the activity for which the clothing will be worn, as well as the environment in which the activity will take place in order to make outfit recommendations, Fig. 1.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".